The second alleges that even if some connectionist robot descended from MIT's COG were to emulate the powers of Sherlock Holmes, it could never explain how it accomplished its feats of inference without using language and deductive logic.

alvin, prior to his [first ever] first-person long-lost-friend experience, doesn't know what it's like to meet a long-lost friend in the flesh.


The elaboration of non-Euclidean geometries required little more than a change of perspective after Giovanni Girolamo Saccheri's purported proof of the parallels postulate by reductio ad absurdum (Saccheri and Halsted 1986).


By contrast, since the stimuli in Trueswell, Tanenhaus, and Garnsey (1994) contained more factors that biased in favor of the relative-clause reading, an interactive model would predict that relative clauses would be easier to understand in these cases.

the corporation fires many of its employees every summer.


Evidence in favor of a radically interactive constraint-satisfaction model comes from the nonstationarity of preferences during sentence comprehension.

two major criticisms of constraint-based models are leveled by Clifton in this volume.


The approach may be more difficult to generalize than Viegas currently envisions, given such common forms as teller, prayer, ~eer, and so on.

in both cases, the authors find that Levin's work is useful as a comprehensive description of subcategorization patterns for lexical implementation, but much less reliable as a predictor of semantic behavior.


Such theoretical elegance appears at first examination to benefit efficiency at implementation time, which is what attracts computational linguistics to these approaches.

burstein, Wolff, and Lu ("Using lexical semantic techniques to classify free-responses") present a very engaging application of lexical semantics to educational testing in the scoring of flee-word test responses.


Two performance values are reported, indicating what percentage of the 100 substrings are words.

it has been noted elsewhere that the English corpus contains quite a few duplicated articles (Paul and Baker 1992).


C code is provided in Appendix C. This section will use the running example shown in Figure 6, where the corpus is: "to_be$or$not_to_be$'.

the suffix array is initialized to the integers from 0 to 17.


The substrings in a class all have the same tf value (property 1) and the same df value (property 2).

(As mentioned above, two substrings in the same class need not have the same MI value.)


The right branch handles cases where words are highly indicative of a topic boundary.

in automatic segmentation systems, word usage cues are often captured by statistical language modeling and information retrieval techniques.


For training the language models, we removed stories with fewer than 300 and more than 3,000 words, leaving 19,916 stories with an average length of 538 words (including stopwords).

there have been some studies of discourse-level boundaries in a computational framework.


Certain phonemes are written using combinations of letters: e.g., sz, cz, and szcz represent phonetic ~, G and ~, respectively.

automatically selecting the "correct" segmentation becomes nontrivial.


(Equating more than one pair of nodes in each derivation step shifts some of the work done in reading off in DSG to the derivation in TDG.)

this choice has mainly been made in order to allow us to derive verb-second matrix clause order using the same d-trees, which is also why the verb is in a component of its own.


Normally, substitution is used in LTAG to associate a complement to its head, and adjunction is used to associate a modifier.

in the following, we consider a tree description to be satisfiable if it is satisfied by a finite tree model.


Furthermore, like DSG, TDG does not allow the conflation of immediate dominance structure specified in elementary structures.

this subsection partially addresses this question and shows how the path constraint expressing the nature of projection from a lexical item can be used to derive the arrangements of components corresponding to adjoining in some cases as well as predict when the nesting condition of adjoining is too limiting in the others.


Looking more in detail, we observe that recall increases with fl --(1-c~), but precision decreases.

we then built another test set, called TSAll, with 400 PNs again reflecting the frequency and category distribution of the corpus.


From the point of view of grammatical theory, however, frameworks based on categorial grammar have usually been problematic either by being too restricted in their expressive power or by not being restricted enough.

for example, we are told that CCGs can be parsed in polynomial time with a worst-case complexity of O(n6), but this depends on results for linear indexed grammars by Weir (1990, 1994) and an assumed equivalence between CCG and linear indexed grammars, which in turn depends on a certain way of interpreting the rule schemata for type raising in CCG.


Chapter 10, finally, summarizes the architecture of the theory as a whole and briefly discusses its role in acquisition and performance.

classical categorial grammar, where the only permissible operation is functional application, is equivalent in expressive power to context-free phrase structure grammar and is therefore considered by most researchers as too restrictive.


(In current parlance, we would most naturally think of this as a path from the root of a trie to one of its terminals, inquiring of each node its associated one-letter entropy, based on the continuations from that node.)

the central idea of minimum description length analysis (Rissanen 1989) is composed of four parts: first, a model of a set of data assigns a probability distribution to the sample space from which the data is assumed to be drawn; second, the model can then be used to assign a compressed length to the data, using familiar information-theoretic notions; third, the model can itself be assigned a length; and fourth, the optimal analysis of the data is the one for which the sum of the length of the compressed data and the length of the model is the smallest.


In work in progress, we have added to the capability of the algorithm the ability to posit suffixes that are in part subtractive morphemes.

the second approach that can be found in the literature is based on the hypothesis that local information in the string of letters (respectively, phonemes) is sufficient to identify morpheme boundaries.


The central idea of minimum description length analysis (Rissanen 1989) is composed of four parts: first, a model of a set of data assigns a probability distribution to the sample space from which the data is assumed to be drawn; second, the model can then be used to assign a compressed length to the data, using familiar information-theoretic notions; third, the model can itself be assigned a length; and fourth, the optimal analysis of the data is the one for which the sum of the length of the compressed data and the length of the model is the smallest.

this is a difficult task to accomplish well, as English illustrates, for we would like to be able to determine that NULL.s is primarily a subcase of 's.NULL.s, and not of (e.g.)


For example, in one English corpus, the stems despair, pity, appeal, and insult appear with the suffixes ing and ingly.

in (10c.ii), we need change in the total length of the pointers to the stems, and this has actually already been computed, during the computation of (9).


The broken character of the corpus serves essentially as an upper bound for the chunks that are postulated, while the letters represent the lower bound.

if there is a regular paradigm in English consisting of the suffixes NULL, -s, -ing, and -ed, we expect to find stems appearing with most possible combinations of these suffixes as well.


In order to see at which point TagPair is overtaken, we have trained several systems on increasing amounts of training data from LOB.

this will let us examine the tagging of a language other than English (namely, Dutch).


The presence of various tagger (dis)agreeement patterns for the four data sets.

the construction of ensembles was based on bagging, selection of different subsets of features (e.g., context and lexical features) in decision tree construction, and selection of different splitting criteria in decision tree construction.


The word is merely another feature in the probability model.

common ways to reduce the error rate are to devise better representations of the problem, to spend more time on encoding language knowledge (in the case of hand-crafted systems), or to find more training data (in the case of data-driven systems).


Each tagger is allowed to vote for the tag of its choice, and the tag with the highest number of votes is selected.

the conjunctions of these features are not all available in order to keep the search space manageable.


This leads to BestLite being always equal to WotanLite for TBL and MBT, and to projected Wotan for MXP and HMM.

no generalizations over groups of words with the same set of potential tags are possible.


With the overgenerated precision and ~6 event removed, we obtain 8 = 0.615 precision and 8 = .267 recall, resulting in an F-score of 0.372.

nonetheless, there are more restrictive parameterizations that come much closer to producing only the correct alignments.


6 For instance, the fact that the ORG_TYPE slot in ORGANIZATION templates has the value COMPANY in 115 out of 117 instances almost ensures that any ORGANIZATION template produced by a system can get matched to an arbitrary one in the key for a given document.

this lesson is important because alignment problems will likely become exacerbated in future evaluations, as the natural language applications addressed become yet more complex.


String fills, shown italicized, are simply strings taken from the text, such as CEO for the POST slot in a SUCCESSION_EVENT.

in the first two of these scenarios, a system change that should have improved F-score decreased it instead.


1995] extracted for this example) are shown in the righthand column.

generally speaking, there can be any number of templates in the key and system response for a given document; all, some, or no pairs of which may be descriptions of the same event.


Precision is the number of correct fills divided by the total number generated by the system, and recall is the number of correct fills divided by the the total number in the key: In the example, there are 8 correct fills, 13 generated fills, and 15 possible fills in the key, resulting in a precision of 0.615 and a recall of 0.533.

this is a substantial reduction from the F-score of 0.571 that the overgenerated event actually receives, a change which implicitly instructs the developer to remove the rules responsible for extracting the correct event.


We followed Chelba (2000) in using an LM weight of 16 for the lattice trigram.

see Figure l(b) for the explicit representation.


One complicating issue has to do with the tokenization in the Penn Treebank versus that in the HUB1 lattices.

interpolation with a trigram model yields an exceptional improvement relative to the improvement observed by other models, demonstrating the degree to which the information captured by our parsing model is orthogonal to that captured by a trigram model.


3 A PCFG also defines a probability distribution over strings of words (terminals) in the following way: The intuition behind Equation 1 is that, if a string is generated by the PCFG, then it will be produced if and only if one of the trees in the set Twg generated it.

then after level 3, there is another branch in the decision tree.


We will then present the algorithm in terms of this relation.

our top-down parser allows for the incremental calculation of generative conditional word probabilities, a property it shares with other left-to-right parsers with rooted derivations such as Earley parsers (Earley 1970) or left-corner parsers (Rosenkrantz and Lewis II 1970).


Then he suggests that "readers who have not had a course in logic or who would like a quick review should read [Appendix A.1]" (p. 11).

this is illustrated by a birthday-party context, which has several levels of nested conceptual graphs, and is now treated as a graphical user interface: "At the bottom of the box in Figure 3.5 is another concept [Process].


Running examples are illustrated by intermediate representations at various stages of processing.

apart from this drawback, there are a number of further issues that one could wish had been addressed in the book.


They demonstrate very well the increasing degrees of precision in which utterances are specified and the associated commitments about possible text portions that express these specifications.

helmut horacek is a senior researcher at the University of the Saarland, Germany.


The preface also lists the topics covered in the collection, reflecting the fact that the majority of the papers deal with research in traditional IR or with architecture and implementation issues.

the architecture of distributed IR systems is considered in "the effect of collection organization and query locality on information retrieval system performance" by Zhihong Lu and Kathryn McKinley.


The most clearly related work within computational linguistics is probably that of Patrick Blackburn on applying modal logic to the description of trees (the work of Blackburn and his associates on describing trees in logical terms--modal logic, in their case--has been developing since about 1993; see Blackburn and Meyer-Viol [1997] for an overview).

there is an exact descriptive-complexity characterization of the context-free languages.


If we reinterpret this as (A --* not-P) ~-~ (not-A ~ P), it is false.

talmy classifies many verbal classifiers, for example, in Atsugewi, as satellites.)


At the heart of this approach is the distance metric A(X, Y) which computes the similarity between instances X and Y.

at the sense level the average polysemy figures are much lower for adjectives and adverbs.


This review has been extremely brief and has not covered large areas of research into WSD.

the evaluation of WSD algorithms has recently become a much-studied area.


However, LDOCE has a higher level of average ambiguity and does not contain as complete a thesaural hierarchy as Roget, so we would not expect such good results when the algorithm is adapted to LDOCE.

the first two sentences contain the ambiguous word well; as an adjective in (1) where it is used in its "state of health" sense, and as a noun in (2), meaning "water supply".


Pr w e context Yarowsky assumed the prior probability of each subject category to be constant, so the value Pr(SCat) has no effect on the maximization in (6), and (7) was in effect being maximized.

we felt that it would be useful to carry out some further analysis of the data.


Then using the discounting scheme described, we have P(damnbritish) = 1/(x + 6) P(damn) = 2/(x+6) P(british) = x/(x + 6) and We seek an x for which P(damn)P(british) > P(damnbritish).

as we argued, the permutation and averaging does have significant beneficial side effects, and if anything, it only eliminates from the point of view of the algorithms the important advantage that real children may be given by their mothers through a specific ordering of the utterances.


We first report the results of experiments using a training methodology of 10-fold crossvalidation repeated 50 times.

for the majority vote, verbs with frequency less than 100 yield an accuracy of 92%, K = .84; verbs with frequency between 100 and 1000, accuracy 80%, K = .69; and for verbs with frequency over 1000, accuracy 90%, K = .82.


We had predicted that the TRANS feature would make a three-way distinction among the verb classes, based on its predicted linear relationship between the classes (see the inequalities in Table 13).

we conclude that the verb classification task is likely easier for very homogeneous classes, and more difficult for more broadly defined classes, even when the exemplars share the critical syntactic behaviors.


We can see here that different frequency groups yield different classification behavior.

this is an important concept, which proposes that "regular" ambiguity in classification--i.e., sets of verbs that have the same multi-way classifications according to Levin (1993)--can be captured with a finer-grained notion of lexical semantic classes.


This is dubbed "the unexpressed object alternation" (Levin 1993), and has several subtypes that we do not distinguish here.

the confusability between unergatives and object-drops becomes slightly worse (errors increasing from 6 to 7).


For example, in an attempt to assess the actual completeness and usefulness of the Longman Dictionary of Contemporary English (LDOCE) entries, Boguraev and Briscoe (1989) found that people assigned a "change of possession" meaning both to verbs that had dative-related subcategorization frames (as indicated in the LDOCE) and to verbs that did not.

an important contribution of the work is the proposed mapping between the thematic assignment properties of the verb classes, and the statistical distributions of their surface syntactic properties.


The slice through the system that we describe lies between the goal-driven establishment of a rhetorically structured presentation plan on the one hand, and the construction of a fully specified layout structure on the other.

the principal purpose of using an RSt-concession relationship is to strengthen one's own argument by showing that one is aware of a possible objection but are already able to dismiss it.


When these contents are filled in, the results is page fragments such as that shown in the lower right of the figure.

since we have the option of inserting a header layout element from above, we discharge this here, giving a layout structure as shown to the left of Figure 18.


As part of my first objective, I learned that speech technology is greatly hampered by limitations on the size of the lexicon that can be handled efficiently in real systems.

as to the second objective stated above, the incorporation of semantics into speech seems a long way off.


If systems are to be deployed into situations where such deficiencies might matter, it might be necessary to find more appropriate evaluation methods.

they report a variety of performance statistics for a set of 41 challenging-looking sentences from German computer magazines.


First, the semantic representation derived from the syntactic structure and lexical items of a sentence may be underspecified.

then he discusses the implications of the theory: first, for the interface between semantics and pragmatics, and second, for syntactic theory.


He argues credibly, however, that a viable everyday writing system must bear a "sensible relationship" to the language it represents, that we can expect a natural pressure in the direction of consistency.

would include the following regular filter to constrain the variants to appear only in appropriate contexts.


To complete the picture, the AT&T finite-state libraries and a set of programming formalisms called Lextools are now available on the Web, which further facilitates the reproduction of his English examples and testing on other languages.

while this approach would definitely seem to work, it is difficult to see how it differs substantially from the following two mapping rules.


Both patterns are abstractions over cases of fragment embedding in which the root of the dominating fragment constitutes one of the relevant occurrences.

sentence (2b) illustrates the case of nonlocal binding (here: outside the embedded sentence), which is admissible only in the case of the nonreflexive pronoun.


The second method (LRC-S) selects the NP that is the subject of the topqevel S structure.


The first noun phrase in a pair is always considered the antecedent, while the second is the anaphor.

similarly, the inadequacy of our system's surface features means that the current feature set may not be enough and more information sources should be added.


By tabulating the number of referential pronouns that are excluded, the format clarifies the composition of the test data set and enables the calculation of the resolution rate (RR), which is a more accurate general measure of performance.

eAll pronouns in the "Out of Scope" category have been explicitly listed, resulting in 0 "Out of Scope" pronouns remaining in the evaluation set.


In general, it appears that cheapness is most likely to be an available option--the expectation that the current Cp will be the next Cb is generally plausible--but salience and cohesion are not always available.

the optimal case, where both salience and cohesion obtain, is Continue; the least preferred is Rough Shift.


--From the publisher's announcement, with minor corrections "Our volume has two goals.


For the sake of the example, consider the following multiclausal sentence from Portuguese displaying backward anaphora between a topicalized reflexive and a pronoun: De si próprio, cada estudante disse que ele gosta.

in the case of the quantificational NP every student, two tags are passed, corresponding to the REFMARK value, providing for e-type anaphora, and the VAR value, providing for bound anaphora interpretations.


For a given ambiguous reflexive, with more than one admissible 6 See the Appendix for the notion of locality and local domain and other auxiliary notions in the definition of binding constraints.

as to reference processing in general, the specification suggested in the previous section provides a suitable framework for the correct representation of the semantically different types of anaphoric links, the range of options not being restricted to coreference only.


It also implies that cases of undesired transitive anaphoricity are handled by other filters during the anaphor resolution process.

this relational constraint returns list B as the value of ANtEC.


constraints involving nonlocal dependencies.

this cannot be viewed as implying that they express grammaticality requirements.


Another problem with precision and recall is that they are not sensitive to near misses.

let a be the penalty for 500 Type A errors, b the penalty for 500 Type B errors, and c the penalty for 500 Type C errors; then the penalty for the FNP segmentation is p = a 2 + b 2 + c 2 .


For average segment size and a uniform distribution of false positives, the average penalty is k 2 , as described earlier.

the behavior at the edges of the segments remains the same, though, so the average penalty never reaches k. Now, consider what happens with smaller segments.


We will call an algorithm complete if it is successful in every situation in which a distinguishing description exists.

experimental research on "minimal cooperative effort" (Clark 1992;Cremers 1996) points in the same direction.


The distribution from even a small corpus shows that we need to estimate various parts of speech for unknown morphemes rather than simply guess them as nouns.

as mentioned earlier, because Korean eojeols have very complex morphological structures, it is necessary to look at the functional morphemes selectively to determine the grammatical relations between eojeols.


An estimation procedure is consistent if and only if the limit of the risk of φ n is 0 as n → ∞ for all θ .

inconsistency is usually viewed as a fatal flaw of an estimator.


In this chapter, Bean and Green present some of their previous research that attempted to typify the relations between a topical guide to scriptures and the passages cited as relevant to the topics in the guide.

the set of all terminal strings of trees in the resultant tree-set will be a context-free language (CFL), and for every CFL there will be such a finite set of trees that generates it under substitution.


(My understanding is that in a lexicalized TAG, each of the trees in T must contain one and only one terminal symbol [word in the dictionary].

here, Dextre Clarke shows how the same word is described incompatibly in different thesauri; she also shows in a table a number of ways in which the


In Chapter 2, "Aspects of the Micro-Structure of Word Meanings," D. Alan Cruse addresses the issue of the extreme context-sensitivity of word meaning, which can result in an almost infinite subdivision of senses.

some tests show the difference in meaning.


The ways in which TAGs exceed the descriptive capacity of context-free grammars seems remarkably close to what one would want in a theory of syntax sculpted for human languages.

it is a refereed collection of papers that were presented in earlier versions at an international workshop on TAGs and related formalisms in Paris in 1994.


They share Fillmore and Atkins's dissatisfaction about dictionary making.

an incentive to read more could have been provided, had each of the four sections been accompanied by a synopsis of its main themes and common threads.


The chapter has little on the relative strengths, weaknesses, and consequences of the adoption of particular mathematical paradigms, which may be frustrating to those asking "Why?"

several annotation schemes have been developed over this period, including both tagsets (see the survey by Leech [1997]) and parsing schemes for syntactic treebanks (see the survey by Leech and Eyes [1997]).


Sampson's team then analyzed this same subcorpus in greater detail, together with a small sample of speech from the London-Lund Corpus, resulting in the SUSANNE Corpus.

i had trouble reconciling those decisions with the tagging of Red as a proper noun (masculine forename) in Red Hogan but as an adjective in V. E. (Red) Berry (page 138).


First, taxonomic hierarchies and semantic networks inherently treat near-synonyms as absolute synonyms in grouping near-synonyms into single nodes (e.g., in WordNet).

solid arrows in the sitspec indicate relations between instances of concepts.


So a word's set of distinctions implicitly differentiates the word from its near-synonyms.

now, the general problem of measuring the semantic distance between words or concepts has received much attention.


Each word in SP has to be chosen from a distinct cluster.

and in the hierarchical structure of Roget's Thesaurus, word senses are ultimately grouped according to proximity of meaning: "the sequence of terms within a paragraph, far from being random, is determined by close, semantic relationships" (page xiii).


Moreover, choosing the wrong word can convey an unwanted implication.

also, the indirectness of expression of each of the peripheral concepts by each of the near-synonyms is not shown in this diagram (but see below).


In the clustered model of lexical knowledge, a difference between two near-synonyms is encoded implicitly in two sets of relative distinctions.

a denotational preference is a tuple consisting of a partial SitSpec and a preferred method of expression, which takes a value on the continuum of indirectness (see Section 5.4).


The figure does not show the representation at the syntactic-semantic level.

the model might look  One possible hierarchy for the various English and French words for untrue assertions.


The lattice consistency condition is imposed on the set of diacritics to ensure category unity.

in parsing, we achieve the same effect by partial execution (Pereira and Shieber 1987 looking for a VP (= S\NP) to the right to become S. The reversal of directionality such as topicalization (e.g., This book, i recommend) requires another schema.


Also, since WordNet is a DAG, Li and Abe turn WordNet into a tree by copying each subgraph with multiple parents.

if the test indicates that the probabilities are sufficiently unlikely to be the same, then the null hypothesis is rejected, and the conclusion is that p(run | canine , subj) is not a good approximation of p(run | dog , subj).


, (5, ⊥ w )}, two cloned states ((0, ) and (1, b)) and two queue states ((⊥, br) and (⊥, bra)).

return false for all symbols a ∈ Σ if δ(p, a) = δ(q, a) return false end for return true end function The function equiv(p, q).


These lexical relationships may be morphological or semantic in nature or both.

semantic relationships cover words that are linked (by synonymy or antonymy, for example); that is, they capture the fact that the words have something in common semantically.


Rather than choosing a fixed n and keeping the n best constituents for each entry in the chart, we chose a probability threshold and kept all constituents within a margin of the highest-probability constituent.

many aspects of our system are still quite preliminary.


In some systems such dependencies are learned from labeled examples (Bikel et al.

the DCA outlined in this article also uses nonlocal discourse context and does not heavily rely on pre-existing word lists.


We also used our DCA system to assign capitalized words, abbreviations, and sentence breaks, retaining only cases assigned by the strategies with an accuracy not less than 99.8%.

• If a period follows an abbreviation and is the last token in a text passage (paragraph, document, etc.


Both these lists can be compiled completely automatically, as explained in section 5.

we deliberately shaped our approach so that it largely does not rely on precompiled statistics, because the most interesting events are inherently infrequent and hence are difficult to collect reliable statistics for.


Since our system is not equipped with a general vocabulary but rather builds a document-specific vocabulary on the fly," important domain-specific words are identified and treated similarly to all other words.

if we detect a context such as Kong., said, this indicates that in this document, Kong is normally written with a trailing period and hence is an abbreviation.


A variety of such lists for many languages are already available (e.g., Burnage 1990).

the standard practice in building SBD software is to disambiguate configurations of a period with its ambiguous local context in a single step, either by encoding disambiguation clues into the rules or inferring a classifier that accounts for the ambiguity of the words on the left and on the right of the period.


this is equivalent to our claim that subordinate clauses are not independent processing units.


The groom hit the best man violently although he.

we saw that the interpretation of anaphoric expressions in certain types of clauses would defy any algorithm based on registers of NPs and a uniform lookup mechanism.


We present an approach that treats the interpretation task as a disambiguation problem and show how we can "re-create" the missing distributional evidence by exploiting partial parsing, smoothing techniques, and contextual information.

one might argue that the preferred analysis for government promotion is "government that is promoted by someone."


Since we have a choice between two outcomes we will use a likelihood ratio to compare the two relation probabilities (Mosteller and Wallace 1964;Hindle and Rooth 1993).

we encode the context surrounding nominalizations using two distinct representations: (a) parts of speech and (b) lemmas.


As the XTAG grammar has been developed by as many as a dozen developers, over a period of more than a decade, such errors are probably unavoidable.

it is totally well-typed if, in addition, every feature F such that Approp(t, F) is defined occurs in A.


Form metrics focus on grammaticality, overall text coherence, and organization and are usually measured on a point scale (Brandow, Mitze, and Rau 1995).

recently, Otterbacher, radev, and Luo (2002) have focused on discourse-based revisions of multidocument clusters as a means for improving summary coherence.


The majority of early extraction research focused on the development of relatively simple surface-level techniques that tend to signal important passages in the source text.

as the articles in this special issue show, domain-independent summarization has several specific, difficult aspects that make it a research topic in its own right.


Of the 632 REF AGENTs, 436 (69%) were classified as US AGENT, 175 (28%) as THEM AGENT, and 20 (3%) as GENERAL AGENT.

scientific texts should make clear what the new contribution is, as opposed to previous work (specific other researchers' approaches) and background material (generally accepted statements).


Third, the importance of a citation has been related to the distinction between authorial and parenthetical citations.

• we base our model on the work of Clark and Wilkes-Gibbs (1986), andHeeman andHirst (1992) .


(1995) and Shriberg (1994), which has these parts: (1) reparandum, (2) interruption point (+), (3) interregnum (editing phase, {.

if the question-answer pair consists of more than two sentences, the linkages are repeated until no further questions or answers can be added to the current linkage cluster.


Anaphora resolution, especially in certain domains, is a bigger issue.

the worst-case values may not reflect the actual performance of our application.


Neither the abstracts nor the source documents were electronically available, so the information was collected through photocopies.

an aNOVa showed statistical differences in the F-score measure at p ≤ 0.01 between the different automatic abstracts (observed F(2, 57) = 5.28).


We think that this Table 14 Coselection between sentences selected by human assessors and sentences selected by three automatic summarization methods in recall (R), precision (P) and F-score (F).

we do not have information regarding number of sentences and words in the source document.


Decomposition of human-written summaries involves analyzing a summary sentence to determine how it is constructed by humans.

(Rule: Adjacent words in a summary are not very likely to come from sentences far apart.)


We also have some doubts, however, as to whether the definitions given in synonym dictionaries such as Gove (1984) do indeed accurately represent how all members of a language community use near synonyms.

we are also grateful to the anonymous reviewers for their helpful comments.


• B cannot write a Java program from scratch, but can make very simple changes to an existing Java program (such as changing a string constant that specifies a URL).

both the cognitive science literature and recent experiments carried out in the SUMTIME project at the University of Aberdeen, of which the current authors are a part, suggest that this may be difficult to do because of variations among people, that is, because the same word may mean different things to different people.


With the aid of an inheritance-based formalism, redundancy in stem descriptions would be kept minimal, thus retaining a logical, but not object-level, autonomy of stem components while accommodating exceptions at the same time.

kiraz himself never questions the tradition that interprets the conceptual autonomy of consonantal root, template, and vowel sequence 1 as technical nonlinearity.


Rather, this book represents a particular concrete example of corpus linguistic investigation, accompanied by a critique of generative linguistics.

i would recommend it as a useful source of inspiration for researchers in the field, as long as their foreseen applications are unaffected by the criticism presented above.


"The book offers a combined discussion of the main theoretical, methodological and application issues related to corpus work.

"Since the early work of Montague, Boolean semantics and its subfield of generalized quantifier theory have become the modeltheoretic foundation for the study of meaning in natural languages.


For example, for a sound system, the requirements that arise from speaking and listening often conflict with each other.

we will have two fitness functions regarding the first criterion, perceptual contrast, denoted as F 1F and F 1S , with the latter based on Schwartz's method and the former based on Fant's.


First, we describe the dependence of alignment positions.

in the following, B ik refers to the kth element of B i in ascending order.


Therefore, lexical correspondences like that of the German compound word Zahnarzttermin with the English dentist's appointment cause problems, because a single source word must be mapped to two or more target words.

we propose to measure the quality of an alignment model by comparing the quality of the most probable alignment, the Viterbi alignment, with a manually produced reference alignment.


If we use HMM as the starting point, we observe a much smaller effect.

• Och and Ney (2000) suggest that the effective multiplicity of a dictionary entry be set to a large value µ + 1 if the lexicon entry actually occurs in one of the sentence pairs of the bilingual corpus and to a low value otherwise: µ(e, f) = µ + if e and f co-occur only dictionary entries that indeed occur in the training corpus have a large effect in training.


It is unlikely that someone would describe an object as "the dog next to the tree in front of the garage" in a situation in which "the dog in front of the garage" would suffice.

in general, there is no a priori reason to assume that our scene representations will be planar.


In terms of the graph perspective, this is the set of vertices in the scene graph G (other than the target vertex v) to which the graph H refers.

we are not forced to say that values of the attribute type are always cheaper than values of the attribute color.


attempts have also been made in computational work to extend a pure Cooper storage approach to handle scope prediction.


They are also similar in addressing only the issue of scope generation, or identifying all and only the accessible readings for each sentence.

the first-and third-ranked features in table 8 show that an intervening comma or colon will make the classifier tend toward class 0, "no scope interaction."


The statistical basis of the model presented in this article offers increased robustness and the possibility of more serious evaluation on the basis of corpus data.

the rules that these systems use in making quantifier scope decisions are motivated only by the researchers' intuitions, and no empirical results have been published regarding their accuracy.


Here, the translation probability φ(0 | e ) and notranslation probability p(f | e ).

the word reordering restrictions and the beam search pruning techniques are directly carried over to the full set of IBM-4 parameters, since they are based on restrictions on the coverage vectors C only.


Although the ultimate goal of the Verbmobil project is the translation of spoken language, the input used for the translation experiments reported on in this article is mainly the (more or less) correct orthographic transcription of the spoken sentences.


Pedagogically, it might also work as a tool for sneaking the ideas of formal linguistics into the minds of people initially more interested in the language itself.

one obvious role is as a guide to English grammar for people who build grammatical artifacts.


it includes four parts: language processing and corpora, linguistic coverage, speech processing, and evaluation and conclusions.


After a new utterance is combined with the previous DRS, the complete newly constructed DRS is translated to first-order logic and checked for consistency, without appealing to previous inference results at all.

it does not presuppose that just any male person has an apartment, nor that some boxer or other creature owns an apartment.


Oversimple text-clauses: We noticed this subtle defect only as a result of experience with the program.

stop using the cream and tell your doctor as soon as possible.


In linguistics, sentence is used mainly as a syntactic category, defined by phrase structure rules such as S → NP + VP.

obviously further texts could be obtained by different wordings of the propositions or different placements of however (e.g., "The FDA bans Elixir; the FDA approves ElixirPlus, however").


In ICONOCLAST we have found it more convenient to adopt the latter policy.

they treat rhetorical structure as part of document structure.


• Constituents A and B are not associated with a discourse connective, because they are elementary propositions, so their value for the connective variable is C ∅ (meaning no connective).


6 WebExp is a set of Java classes for conducting psycholinguistic experiments over the World Wide Web.

recall from Section 2.2 that thresholding is an option for the counts f (v, e) and f (o, e).


it is beyond the scope of the present study to develop such a system.


c. Peter hat den Gipfel zu erreichen versucht.

we obtained the frequency f (difficult, e) only on the basis of infinitival constructions (see the examples in (54)).


The occasional typographical error should certainly be overlooked in view of the bargain price, which should allow many readers to buy copies of their own.

descriptions of the Metathesaurus, Semantic Network, and SPECIALIST lexicon are given, together with an example of how UMLS can be used to create specialized semantic networks for a single concept (e.g., heart).


This points to sublanguage-specific application development's being substantially simpler than general-language application development.

also, the focus is usually on content words and topics or domains, with other differences of genre or sublanguage remaining unexamined.


Later versions of PTMiner include a final filtering stage to clean the extracted corpus; Nie and Cai (2001) independently used features similar to those described here to eliminate noisy pairs.

16 Step 3 is potentially tricky owing to computational complexity issues.


The translations in Table 5 are provided by the translation models P(e | f ) and P(f | e).

the file pairs identified in previous steps are further verified in regard to their contents.


Now, if a query is more probable given a language model based on document D 1 than given a language model based on document D 2 , we can then hypothesize that document D 1 is more likely to be relevant to the query than document D 2 .

for a more detailed discussion of the relationships between the classical (discriminative) probabilistic IR models and recent generative probabilistic IR models, we refer the reader to Kraaij and Spitters (2003).


The words added to the query during query expansion do not need to be strict synonyms to improve the query results.

the corresponding precision at this rank number is defined as the number of relevant documents found in the ranks equal to or higher than the respective rank r divided by n. Relevant documents that are not retrieved are assigned a precision of zero.


Although "reverse" parameters can theoretically be obtained from the model through Bayes' rule, it is often more practical to train two separate models if both directions are needed.

the availability of this corpus is much more restricted than the Canadian Hansard.


Our sentence alignments are carried out by a program called sfial, an improved implementation of the method described in Simard, Foster, and Isabelle (1992).

as we explained in Section 1, there are theoretical motivations for embedding translation into the retrieval model.


there are sometimes "larger" couples, as when a single sentence in one language translates into two or more sentences in the other language (1-to-N or N-to-1), or when sentences translate many to many (N-to-M).


Like most EBMT systems, our approach suffers from the problem of "boundary friction": where chunks from different translation examples are recombined, the quality of the resulting translations may be compromised.

the problem of segmentation is not an inconsiderable one in all EBMt systems, but we (and others) have found that using the marker hypothesis can greatly facilitate such a process.


Table 1 shows the results obtained from our human evaluators' ratings of the translations produced by our system when it was populated with fragments derived from one of the individual on-line MT systems.

in sum, therefore, the best results are obtained when chunks from all three on-line systems are used, and the system's databases are seeded with translations from these systems for both third-person singular and third-person plural versions of sentences.


Instead, we take advantage of the fact that the initial phrasal chunks correspond to rule right-hand sides.

rather than search for competing candidates, we select the "best" translation and have its morphological variants searched for on-line.


evaluated: Of course, if we consider (say) three-chunk combinations from either system A or B, the only possibilities are AAA or BBB, respectively.

the noun phrases in the test set also need to be fragmented using our n-gram segmentation method, as it is highly probable that they do not exist en bloc in the phrasal lexicon and therefore need to be analyzed using smaller fragments in the system's databases.


With respect to translation quality, whereas Logomedia and Reverso could hardly be distinguished when it came to numbers of translations of sentences adjudged as intelligible and syntactically correct, if we consider those translations considered unintelligible by our human evaluators, about twice as many translations produced by chunks derived from Reverso were unintelligible compared to those produced by Logomedia.

consider the examples in (45): (45) Input: an old story in common System B: une vieille histoire dans commun System combination Bc: une vieille histoire en commun the optional PP in common was mistranslated by system B as dans commun, but when knowledge from system c is added to that of system B, the improved translation en commun is generated.


Finally, we comment on the relative strengths and weaknesses of the three on-line MT systems used.

the count for the second bigram in each example in (52) can of course be discounted, as the juxtaposition of sont or est with au bears no relevance to the correctness or otherwise of the translations in (51).


From the syntactic analysis provided by the parser, we extracted all occurrences of bigrams that were attested both in the BNC and the NANTC corpus.

we employed the same pseudodisambiguation method to test whether web-based frequencies can be used for distinguishing between seen and artificially constructed unseen bigrams.


The extraction is straightforward: When a word sense w i has an associated directory d, we scan the site descriptions on the ODP page that corresponds to the directory d and extract all contexts in which w occurs, assuming that in all of them w is used in the sense i.

the goal is to compare the performance of a supervised word sense disambiguation system using Senseval 2 training data (hand made for the competition) to that using the sense-tagged corpus from ODP (automatically extracted).


In spite of being manually supervised, Web directories offer immense structured corpora that deserve our attention as sources of linguistic information.

as all other components of v are null, the highest value of the components different from sense 1 is also null (max i =j (v i ) = 0); therefore, C 4 = 1.


These ambiguities, combined with a very sparse collection of bibliographic citations, may make it difficult for novices to find clarification elsewhere.

the chapters by Niyogi, Turkel, and Briscoe (collectively, NTB) all base their simulations on an innate Universal Grammar-the general form of grammar is built into the agents, and the lexicon is given little or no importance.


it provides a general survey of monolingual English dictionaries, including a brief history of their development, and a detailed investigation of the nature of learners' dictionaries and their special features.


The four main chapters of the book then cover document retrieval from collections and from the Web, information extraction, text categorization, named-entity and coreference recognition, and automatic summarization.

a reader who completes the book will be well-equipped then to learn more by reading selectively from the textbooks and handbooks.


There were 58 sentences (29.0%) in this group.

this set of rules will produce all possible bracketings of the input IG sequence, subject to the constraints specified by the patterns.


Placing the subordinate conjunction (structural connective) because between the two clauses provides more evidence for this rela-tion.

the resulting proposition after(e 4 ,e 2 ) would be given its own index, e 6 , and added to the discourse context.


The resulting λ-expression applies to the interpretation of the matrix clause of otherwise, resulting in the conditional's being added to the discourse context: Here the relevant elements of (38b) and (39b) can be represented as α = otherwise R α = if S 38 = you get a ticket σ 38 = e 3 , where e 3 :get ticket(you) S 39 = go straight on σ 39 = e 3 , where e 3 :go straight(you) The unresolved interpretations of (38b) and (39b) are thus: As we showed in section 2.2, different ways of resolving the anaphoric argument lead to different interpretations.

in (38), the conjunction because is made explicit (example (40)), and in (39), the connective is simply and or but (example (41)).


One clear example is so, conveying result.


here the receiver denotes the receiver associated with (by virtue of being part of) the already-mentioned phone Myra darted to.


both structures are directed acyclic graphs (DAGs), with acyclicity the only constraint on what nodes can be connected.


It is important to stress here that the level of representation we are concerned with is essentially an LF for discourse.

at this level, these predicates anchor initial trees into which clauses substitute as arguments.


The probability of NPB(dog) → DT(the) NN(dog) would be estimated as 11 In making the independence assumption the model will fail to learn that the STOP symbol is very likely to follow a determiner.

the next section considers the distributional properties of the rules in more detail.


Knowledge about subcategorization preferences (for example, that a verb takes exactly one subject) is also required.

the model will assign unreasonably high probabilities to NPs such as [NP yesterday the dog] in sentences such as Yesterday the dog barked.


The tokenizer comprises a small set of manually developed finite-state rules for identifying word and sentence boundaries.

we also investigate use of the one-senseper-discourse heuristic to propagate a sense tag for a word to other occurrences of the same word within the current document in order to increase coverage.


These groups are formed such that they partition all adjective synsets.

we obtain a probability estimate for each nc to which the target noun belongs, using the distribution of the TCM associated with the co-occurring verb or adjective and the grammatical relationship.


This is first done in the tradition of inductive approaches to TC: Training examples are used to induce the maximum margin hyperplane that separates positive from negative examples in a binary setting.

some other dimensions exist where further improvement should be looked for.


Paşca's approach is to extract the words connected to the wh-word in the dependency representation of the question and look up those words in WordNet conceptual hierarchies that have been hand-mapped onto specific answer types.

indeed, Moldovan, Harabagiu, and colleagues earned the top score in the TREC 2002 QA track using a technique not described in Paşca's book: automated reasoning based on lexical chains derived from Word-Net glosses (Moldovan et al.


Furthermore, one cannot rely on the question (wh-) word to determine the answer type.

temporal databases include support for temporal information, so entries can be evaluated with respect to associated times and the time of the query.


The two processes look at different types of information in data and exchange the information in learning.

(We will denote this implementation as MB-B hereafter.)


Lines 14-15 show that for each class t, we choose only the top b classified instances (in terms of the posterior odds), which are then stored in Q t .

with the frequency data f (c, t ε ) and EM we can estimate P (C) (e ε | t ε ).


CorMet uses gradients in selectional preferences learned from dynamically mined, domain-specific corpora to identify metaphoric mappings between concepts.

it is unsurprising that there are no particular mappings between two sets of miscellaneous administrative and policy documents.


the verb study is ambiguous among three classes when attested in the transitive frame: Learn (see example (8a)), Sight (see example (8b)), and Assessment (see example (8c)).


Dorr and Jones show that 97.9% of the semantic classes can be identified uniquely by their syntactic signatures.

in agreement with Chao and Dyer and Ciaramita and Johnson, we show that prior knowledge about class preferences improves word sense disambiguation performance.


Possible modules involved are the lexicon, speech recognition, syntactic parsing, speech synthesis, semantic interpretation, and so on.

consider how to extract the word from these four sentences.


We will show that even compared to such a deliberate approach, our simple method is marginally better.

it is also impossible to approach traditional precision and traditional recall by comparing the hand-segmented sample sentences and the automatically segmented sentences, as people usually do, because our method does not touch upon segmentation.


But for a given value of P(A), the larger the value of P(E), the lower the value of κ.

but for a given value of P(A), decreasing P(E) will increase κ Co , leading to the paradox that κ Co increases as the coders become less similar, that is, as the marginal totals diverge in the contingency table.


κ assessments for discourse and dialogue tagging are most often performed on the same portion of the data, which has been annotated by each of a small number of annotators (between two and four).


A more serious criticism of Readings in Machine Translation is that the book is somewhat dated.

the good experimental results for the language modeling approach reported throughout this book may be due more to its improved statistical estimation techniques than to the use of language modeling as a theoretical framework.


This seems to be the approach taken by Passonneau (1998); her definition of a null Cb seems to imply that a Cb must be (noninferable and) explicitly realized, and her null Cbs constitute the cases in which there is no explicit Cb.

this has no effect on the analysis presented in this article, except for the fact that had we separated these clauses, it would have made the problematic situation we describe later even more marked.


The proposed transition types provide a characterization of a previously unaccounted-for situation in centering theory, namely, the coherence of sequences of utterances containing inferable Cbs.

we describe this problem and propose two additions to the usual repertoire of transitions that enable a more authentic account of coherence in this corpus, while remaining within a centering framework.


It includes the cases in which the Cb of U i−1 might be "?," that is, that U i−1 might be the discourse-initial utterance or might simply have no Cb, while U i does have (an explicit) Cb.

the definition proposed by Grosz, Joshi, and Weinstein allows inferable entities, that is, entities that are not expressed at the surface level of the utterance or immediately recoverable from the subcategorization properties of the verb (as, for example, zero pronouns are) to constitute centers of an utterance.


At the level of coverage provided by the Gainen Base, however, we cannot usefully assess how well semantic similarity characterizes coherence in this corpus as a whole.

in terms of semantic distance as determined by the Gainen Base, we suggest that a sufficiently low similarity measure might characterize both COMPLETE SHiFTs and the beginning of a new discourse segment.


22 It is not clear to what use we might put this more detailed information about the varying levels of strength of connection among utterances; however, it parallels the observation in Fais (2001) that some e-mail authors make hierarchical distinctions in marking paragraphs in their messages, using line breaks to separate utterance clusters having some semantic connection and full spaces to separate utterance clusters having weak or no semantic connection.

in our account, utterances that have a coherent relationship to the immediate context of discourse may nonetheless have no Cb; that is, the presence of a Cb is, in our approach, only a sufficient condition.


In absence of semantic interpretation, it is not possible to fully capture conceptual relationships between concepts (for example, the taxonomic relation between bus service and public transport service in Figure 4).

the main contribution of this work is in the area of ontology engineering, although machine-learning methods are also proposed to automatically enrich the ontology with semantic relations.


For example, Vossen (2001) uses statistical methods and string inclusion to create lexicalized trees, as we do (see Figure 4).

the SSI algorithm consists of an initialization step and an iterative step.


We still need to produce an in-field evaluation of the improved readability of the ontology enriched with textual definitions.


There was no overlap between training and test sets, and the vocabulary was closed.

a technique for inferring finite-state transducers is proposed in this article.


Some of these techniques are based on results from formal language theory.

nevertheless, there are many techniques for inferring regular grammars from finite sets of learning strings which have been used successfully in a number of fields, including automatic speech recognition (Vidal, Casacuberta, and García 1995).


In this work, (smoothed) n-grams are used.

the Viterbi score VT P for the first pair is 1.0 • 0.3 • 1.0 = 0.3, which is lower than the Viterbi score VT P for the second pair, 1.0 • 0.4 • 1.0 = 0.4.


The upshot is that DOP models can capture many kinds of statistical correlations that standard models cannot.

these chapters are more likely to be of interest to those who already know the majority of what's in parts I and II.


Let Y represent the set of parses for Y * ⇒ w i .

for example, Henderson and Brill (2002) consider the case in which acquiring additional human-annotated training data is not possible.


For example, if scores have been assigned by f conf , then we know that the confidence intervals of the candidates similar to x must decrease slightly after learning x.

in addition to PP-attachment, as discussed in this article, sample selection has been successfully applied to other classification applications.


Performance of the hypothesis: Testing the candidates on the current working hypothesis shows the type of input data on which the hypothesis may perform weakly.

initially, the training set, L, consists of a small number of labeled examples, based on which the learner proposes its first hypothesis of the target concept, C. Also available to the learner is a large pool of unlabeled training candidates, U.


Mapping it back into the entropy definition from equation 3, we derive the tree entropy of w as follows: Using the bottom-up, dynamic programming technique (see the appendix for details) of computing inside probabilities (Lari and Young 1990), we can efficiently compute the probability of the sentence, P(w | G).

the early learning rate of f conf is as poor as that of f novel .


The percentage of RET is even greater than with IF (114 [17.0%], almost twice the percentage of CON [9.4%]) as is that of Rough Shifts (100 [14.9%], almost three times the percentage of Smooth Shifts [5.2%]).

rank: rank CFs according to grammatical function, linear order, a combination of the two as in Gordon, Grosz, and Gillion (1993), or information status as in Strube and Hahn (1999).


Our second major finding is that parameters do matter: that is, it is possible to set the parameters in such a way as to make all three claims verified in a statistical sense.

we also found a trade-off between Rule 1, on the one hand, and Constraint 1 and Rule 2, on the other: Setting the parameters to minimize the violations of local coherence leads to increased violations of salience, and vice versa.


Rule 2 (BFP), formulated in terms of single transitions, accounts for larger percentages of the data (single utterances) and was found to be verified both with the vanilla instantiation and with the "best" instantiations.

we also saw that in languages such as Italian, Japanese, and Turkish, the preferred realizations of CBs are morphologically null elements (Kameyama 1986;walker, Iida, Cote 1994;Turan 1998;Di Eugenio 1998).


This claim is further supported by the evidence concerning the Repeated Name Penalty (Gordon, Grosz, and Gillion 1993).


As the name suggests, smoothed precisionq f (j) is a smoothed version of (raw) precision q f (j), which is the probability that rule f → j is correct given that it matches where Λ f is the set of labeled examples that possess feature f , and Λ fj is the set of labeled examples with feature f and label j. Smoothed precisionq(j|f ; ) is defined as follows: We also writeq f (j) when is clear from context.

recall that In the training step (2.1) of the algorithm, we hold φ fixed and change π, and in the labeling step (2.2), we hold π fixed and change φ.


DL-1-VS (Table 9) uses smoothed precision, but unlike DL-0, DL-1-VS does not use a fixed smoothing constant ; rather varies from feature to feature.

y-1/DL-EM reduces H; the others reduce K. y-1/DL-EM-Λ EM inner loop that uses labeled examples only y-1/DL-EM-X EM inner loop that uses all examples y-1/DL-1-R Near-original yarowsky inner loop, no smoothing y-1/DL-1-VS Near-original yarowsky inner loop, "variable smoothing" yS-P Sequential update, "antismoothing" yS-R Sequential update, no smoothing yS-FS Sequential update, original yarowsky smoothing restricts the data sets on which the algorithm can be shown effective, but also for additional internal reasons.


An interesting range of update rules work in the sequential setting.

the YS algorithms resemble the "Yarowsky-Cautious" algorithm of Collins & Singer (1999), though they differ from that algorithm in that they update a single feature in each iteration, rather than a small set of features, as in Yarowsky-Cautious.


Recall thatŷ ≡ arg max j π x (j), where we continue to assume π x (j) to have the "mixture" definition (equation 12).

let us define Note that φ x satisfies the formal requirements of a probability distribution over labels j: it is a point distribution with all its mass concentrated on Y x .


Viral skin disorders are relieved by aliprosan.

karamanis (2001), kibble (2001), and Beaver (2004), have argued for a ranking of the centering principles as opposed to weighting, and indeed Beaver provides a unified formulation of the centering rules and constraints as a ranked set of OT constraints.


We have simplified matters somewhat for the purposes of this implementation.

although this article focuses on centering issues, it is important to remember that other aspects of text quality are evaluated at the same time: The aim is to compute a global measure so that disadvantages in one factor can be weighed against advantages in another.


If we perform log-linear interpolation of a trigram model with a class-based 5-gram model, we observe an additional small improvement in translation quality to an mWER of 30.9%.

second, in histogram pruning (steinbiss, Tran, and Ney 1994), we maintain only the best N p hypotheses.


A standard vocabulary had been defined for the various speech recognizers used in Verbmobil.

in general, the criterion for recombining a set of nodes is that the hypotheses can be distinguished by neither language nor translation model.


Typically, it is necessary to restrict the alignment template length to keep memory requirements low.

the resulting Viterbi alignment of the standard alignment models has a systematic loss in recall.


Collins' thesis discusses a case in which the initial head is modified when it is found to denote the right conjunct in a coordinated phrase.

for example: Since gap augmentations are present only in Model 3, the gamma function effectively is the identity function in the context of Models 1 and 2.


These expectation maximization (EM) experiments were motivated by the desire to overcome the limitations imposed by the heuristics that have been heretofore used to perform head-lexicalization in treebanks.

with arguments identified as described in Section 4.9, if a subjectless sentence is found to have an argument prior to its head, this step detransforms the SG so that it reverts to being an S. Head-finding is discussed at length in Collins' thesis, and the head-finding rules used are included in his Appendix A.


2 Specifically, this article describes all the as-yet-unpublished details and features of Collins' model and some analysis of the effect of these features with respect to parsing performance, as well as some comparative analysis of the effects of published features.

an interesting modification to the standard beam in Collins' parser is that for chart items representing NP or NP-a derivations with more than one child, the beam is expanded to be 10 4 • e 3 .


w L i and t L i are, respectively, the headword and its part of speech of the nonterminal L i .

when some right modifier R i is generated, the previously generated modifier on the right side of the head, R i−1 , is never a punctuation preterminal, but always the previous "real" (i.e., nonpunctuation) preterminal.


We describe the details of this unknown-word mapping in Section 6.9.2.

for example, in figure 11, where we assume fido is a low-frequency word, the trainer would derive counts for the smoothed parameter when collecting events that condition on fido, such as the parameters the word would not be mapped.


The expanded versions go a long way toward making the individual contributions more convincing and easier to replicate.

kracht's book gives a uniform introduction, which currently does not exist at this level, to an important area of mathematical linguistics.


Chatterbots coupled to Microsoft Agent are not the future of ECAs, and this imper-fect account of their moderate success (if only in engaging the casual user) is only possibly worth a second look.

"The mechanisms for referential identification of ellipted arguments are extremely intricate but by no means as elusive as the commonly held view would have it.


This volume contains 15 papers from a special session held at the 11th CUNY Sentence Processing Conference (1998).

it very probably is a great starting point for some enjoyable undergraduate projects.


Figure 4 shows a very rudimentary attempt to model discourse structure using topic/comment relations across sentences, shown using T or C with numbers for concepts.

the linguistic structure leads to a contrastive summary emphasizing key book-type features (LS), the communicative structure to a justification for action summary (CS), and the world structure to a simple descriptive summary (WS), without the presentational or functional character of the other two.


As z Y V, the functions f ðzÞ ¼ e Àz and f ðzÞ ¼ logð1 þ e Àz Þ become increasingly similar, because log ð1 þ e Àz Þ Y e Àz as e Àz Y 0.

a second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence.


Step 2: For t = 1 to N (the number of iterations N will be chosen by crossvalidation) a: Note that this is essentially the idea behind the ''boosting'' approach to feature selection introduced in section 3.3.

the base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses.


Again, the loss function is a function of the margins on training data: Note the similarity of equation 10to the ExpLoss function for classification in equation (6).

for each k we define the following sets: A + k is the set of training examples in which the kth feature is seen in the correct parse but not in the competing parse; A À k is the set in which the kth feature is seen in the incorrect but not the correct parse.


In this case, FrameNet's COMMERCE frame includes roles for Buyer (the receiver of the goods) and Seller (the receiver of the money) and assigns these roles consistently to two sentences describing the same event: PropBank requires an additional level of inference to determine who has possession of the car in both cases.

if for any reason I don't have the values, then I won't recommend it.


This article is focused on the latter application.

then, we consider two words to be similar if they occur with at least a pair of similar positions.


We inherit the intuitive definition assumed in the linguistic tradition of dependency grammar (Hudson 2003).

second, it is also claimed that the set of words occurring in similar positions defines extensionally the requirements associated with these positions.


Another important objective of the article is to use the semantic requirements to capture contextually relevant semantic similarities between words.

consider the binary dependencies shown in the first column of Table 2, which represent the expressions to ratify the law and long dinner.


Considering also that cond stands for the specific property being a nominal phrase (np) whose head denotes a legal document (abbreviated by doc), then the pair bbof_right, ratificationÀ, docÀ means that the particular position ratification of [_ ] selects for nouns denoting legal documents.

the description of the parser remains beyond the scope of this article; it has been described in Rocio, de la Clergerie, and Lopes (2001).


The learning strategy described in the remainder of the article takes advantage of the corequirement structure.

recall growth is more significant in smaller partitions.


They consider Pustejovsky ''as a representationalist, antiformalist ally,'' but they never explain why they consider lexical semantics incompatible with formal semantics.

the body of a theory ''is a set of its statements, variously referred to as laws, propositions, regularities, theorems, or rules. ''


Her areas of interest are discourse planning, argumentation, plan recognition, question answering, and agent modeling.

from a methodological point of view, an underlying assumption seems to be that rule-based and qualitative reasoning should be used for selecting among alternatives at each stage of the argumentation process.


The aspects in question include the argumentation interaction, which is typically a dialogue; the construction and presentation of arguments for or against a proposition; the interpretation of arguments; the knowledge representation required to build and interpret arguments; theory of rhetoric; and applications such as agent negotiation, legal argument, and decision-support systems.

argument schemas, which are such an advance and pervade the entire book, are briefly reintroduced in every chapter, instead of being presented in one authoritative chapter.


Although this is computationally the easiest approach, the resulting PFA may be a very inaccurate approximation of the probability distribution described by the input PCFG.

we can, however, always compute a minimal deterministic FA equivalent to an input FA.


this means that the contribution of versuchen is interrupted twice by arguments of verspricht (by Kunde and er).


, n k , also the v i can be moved leftward, as long as they do not permute among themselves.

and there is a γ (4) 2 ∈ Γ 4 with unvisited mother node.


In this respect, the elementary trees differ from what is usually done in TAG.

in the case of other RSN-MCTAGs, the restriction to a certain arity might exclude certain TAG derivation trees and thereby decrease the language generated by the grammar.


Is it a word, a word sense, or a group of word senses?

for example, the following meanings could theoretically be derived from the meanings 3 and 4, respectively, above (but not directly from concepts (C1) and (C2), respectively): 5. any division of something into smaller units We continue with another example, this time from french.


Section 4.1.4 provides a short summary of the statistical results on crossed dependencies.

that percentage is reduced even further, to 0.84%, by removing all elaboration and similarity relations from the database.


Some proponents of tree structures assume that trees are easier to formalize and to derive than less constrained graphs (Marcu 2000;Webber et al.

correlation between percentage of removed coherence relations and mean in-degree of remaining nodes.


To us, the work we present here seems to be the crucial first step in avoiding arbitrary constraints on inferences for building discourse structures.

section 2.3 for the definition of these terms), kappa values ranged from 0.695 to 0.882; for assigning types of coherence relations, they reported kappa values ranging from 0.624 to 0.823.


Each set of statistics was calculated for both annotators separately.

table 11 shows the data from Figure 17, ranked by the factor of "percentage of overall coherence relations" by "percentage of coherence relations ingoing to nodes with multiple parents."


Instead they use percentage agreement to arrive at this conclusion.

this suggestion is founded on some misunderstandings of the role of agreement measure in reliability studies.


The task of auxiliary placement is alleviated by the presence of features stored in the input nodes.

the change in optimization criteria introduces differences in the similarity measure-specifically, the relaxation of certain constraints-and the search procedure, which in our work uses dynamic programming.


4.25 Palestinian militants fired machine guns and antitank missiles at a bulldozer.

the judge also had access to the original theme from which these sentences were generated.


8 We further constrain the insertion procedure by inserting only trees that appear in at least half of the sentences of a theme.

sentence intersection in a set-theoretic sense produces poor results.


Our pattern-based method, though, seems to work on very large corpora only.

as most antecedents are hyponyms or synonyms of their anaphors in other-anaphora, for each anaphor ana, we look up which elements of its antecedent set a anaid are hyponyms/synonyms of ana in WordNet, considering all senses of anaphor and candidate antecedent.


, algoWN v2n is better than baselineSTR v2n ), showing that they add 39 Some of this difference might be due to the corpus used instead of the phenomenon as such.

also, number matching provides consistent improvements.


(16) Conseco Inc., in a move to generate about $200 million in tax deductions, said it induced five of its top executives to exercise stock options to purchase about 3.6 million common shares of the financial-services concern.

first, we assume perfect base-NP chunking and expect results to be lower with automatic chunking.


For example, bridging exploits meronymy and/or causal relations (among others).

an anaphor and an antecedent candidate were said to agree in number if they had the same value or if at least one of the two values was unknown.


The solution we have adopted in algoWeb v4n is to use Web scores only after string matching, thereby making the Web-based approach more comparable to the WordNet approach.

this holds, for example, for demonstratives and such-anaphors.


In Example (3), the NP other, far-reaching repercussions refers to a set of repercussions excluding increasing costs and can be paraphrased as other (far-reaching) repercussions than (increasing) costs.

the described back-off will most of the time just amount to a recency back-off.


They include my old friends and long-time collaborators, Ron Kaplan and Lauri Karttunen.

there is nothing unworthy in such an enterprise.


First, the condition for making a transition in a given network can be that the symbols starting at the current state in the string are acceptable to some other network named in the transition.

in everyday terms, unification can be thought of like this.


In our experiments, the development-set similarity using the harmonic mean in the additive MI-based CRM was 0.312 for high-frequency nouns and 0.153 for low-frequency nouns, and the development-set similarity using the harmonic mean in the additive t-test based CRM was 0.294 for high-frequency nouns and 0.129 for low-frequency nouns.

features that are shared to a similar extent are considered more important in the calculation of distributional similarity.


Each of n's neighbors, m, is given a vote that is equal to the difference in frequencies of the co-occurrences (m, v 1 ) and (m, v 2 ) and that it casts to the verb with which it occurs most frequently.

the primary goal, however, is to understand how different characteristics relate to high performance in different applications and thus explain why one measure performs better than another.


As such, we might expect that dist α will be a high-recall measure, since recall is calculated over the co-occurrences of w 2 .

since the use of the CR framework requires parameter optimization, here, we randomly select 60% of the nouns to form a development set and use the remaining 40% as a test set.


Mary (visited (their cottage with a thatched roof)).

thus the L 1 distance between two words, w 1 and w 2 , can be written as: noting the algebraic equivalence A + B − |A − B| ≡ 2 • min(A, B) and using basic probability theory, we can rewrite the L 1 Norm as follows: min(P(c|w 1 ), P(c|w 2 )) > 0 if and only if c ∈ tP.


Most likely, these connections were produced because of a lack of better alternatives.

from each initial, out-of-domain corpus, we learn a dictionary.


Alignments computed using this model and a noisy, automatically learned, dictionary will contain many incorrect links.

the IBM-1 alignment model takes no account of word order and allows a source word to be connected to arbitrarily many target words.


shortcomings of the model used to generate the alignments.

we obtained comparable corpora from the web by going to bilingual news websites (such as Al-Jazeera) and downloading news articles in each language independently.


Discovering these links automatically is clearly non-trivial.

we use a version that is augmented with two simple heuristics that attempt to alleviate some of these shortcomings.


We now plug in each of these classifiers (and their associated dictionaries) in our extraction system (Section 2) and apply it to our comparable corpora.

for our classification problem, we have: where c i is the class (c 0 ="parallel", c 1 ="not parallel"), Z(sp) is a normalization factor, and f ij are the feature functions (indexed both by class and by feature).


We first index all the English documents into a database.

the experiments fit in the framework of interest described in Section 1, which assumes the availability of (limited amounts of) out-of-domain training data and (large amounts of) in-domain comparable data.


To formally capture this notion, we parameterize the syntax-aware jump model according to the types of phrases being jumped over.

the sentence alignment problem is a comparatively simple problem to solve, and current approaches work quite well.


In all these numbers, care should be taken to note that the humans were instructed to produce phrase alignments only when word alignments were impossible.

the parameters µ and σ 2 are estimated by computing the mean jump length in the expectations and its empirical variance.


Intuition (as well as further The syntactic tree for an example document sentence.

we will typically bound the maximum length of a phrase; we are unlikely to otherwise encounter enough training data to get reasonable estimates of emission probabilities.


The segmentation of the gold test set depends upon a reference lexicon, which is the combination of several lexicons that are used in Microsoft applications, including a Chinese text input system (Gao et al.

in fact, different Chinese NLP-enabled applications may have different requirements that request different granularities of word segmentation.


Class model parameters are updated in a simple additive fashion: Parameters are altered according to the gradient with respect to MSELoss i (λ λ λ).

it explores high-level linguistic knowledge such as syntactic structure for Chinese word segmentation and NER.


For example, MSRSeg achieves better average performance than the other three segmenters that report results on all four data sets (i.e., S03, S11, CRF).

to remedy this problem, Sun and tsou (2001) revise the definition as follows: Definition 3 A character string AB is called a combination ambiguity string (CAS) if A, B, and AB are words, and there is at least one context under which the segmentation A/B is plausible both semantically and syntactically.


The performance of these methods thus depends to a large degree upon the coverage of the dictionary, which unfortunately may never be complete because new words appear constantly.

comparison of performance of MSRSeg: The versions that are trained using (semi-)supervised iterative training with different initial training sets (Rows 1 to 8) versus the version that is trained on annotated corpus of 20 million words (Row 9).


Here, N is the number of tokens of a particular construction found in a corpus, e.g., the number of tokens of all nouns ending in -, and n 1 is the number of types of that construction, e.g., the number of unique nouns ending in -.

we conclude that our method significantly outperforms the rule-based approaches.


MSRSeg is a hybrid system that takes advantage of both approaches.

we believe that these two issues are not separate in nature and are better approached simultaneously.


Suppose we have a general predefined standard according to which we create a large amount of training data.

even with a much smaller adaptation data set (e.g., 250K words), we still outperform the best Bakeoff results.


In R (www.r-project.org) notation, we can express this model as a=0.2 # acceptance rate s=400 # submissions r=3 # reviews per paper g=0.7 # mixture of good to random reviews gold = rbinom(s, 1, a) good = matrix(rbinom(s*r, 1, g), ncol=r) score = good*gold + (1-good)*matrix(rbinom(s*r,1,a),ncol=r) accept = rank(jitter(apply(score,1,sum)))>(s*(1-a)) precision = sum(accept * gold)/sum(accept) recall = sum(accept * gold)/sum(gold) According to this model, recall can be improved in at least three ways: r Plan A: Increase a (acceptance rate) r Plan B: Increase r (reviews per paper) r Plan C: Increase g (mixture of good to random reviews) We ought to do all of the above, as much as possible.

as for Plan C, of course, life would be good if reviewers were better than they are.


The two experiments did indeed show that such specific corpora allow the extraction of a more efficient semantic space.

bellegarda (2000) observed in speech recognition tasks that such a semantic space is definitely less effective.


The nine types of relations defined on the noun subnetwork, in addition to the synonymy relation that is implicit in each node are: the hyponymy (IS-A) relation, and its inverse, hypernymy; six meronymic (PART-OF) relations -COMPONENT-OF, MEMBER-OF and SUBSTANCE-OF and their inverses; and antonymy, the COMPLEMENT-OF relation.

also, the coefficients reported by Resnik and Lin are actually based on only 28 out of the 30 Miller-Charles pairs because of a noun missing from an earlier version of WordNet.


Charles's experiment with 10 subjects and found that the average correlation with the Miller-Charles mean ratings over his subjects was 0.8848.

malapropism detection was viewed as a retrieval task and evaluated in terms of precision, recall, and F-measure.


While similarity of denotation might be inferred from similarity of distributional or co-occurrence behavior (Dagan 2000;Weeds 2003), the two are distinct ideas.

a number of researchers, such as Dagan (2000), have promoted the hypothesis that distributional similarity can act as a useful proxy for semantic relatedness in many applications because it is based on corpus-derived data rather than manually created lexical resources; indeed, it could perhaps be used to automatically create (first-draft) lexical resources (Grefenstette 1994).


On the graphs of the computed ratings, these empty bands correspond to regions with at most a few points-no more than two points for the Miller-Charles set and no more than four for the Rubenstein-Goodenough set.

if a given measure of semantic relatedness is good, we expect the proportion of false alarms to reduce more considerably -far fewer false suspects will become alarms than correct suspects -thus resulting in higher precision for detection than for suspicion (cf.


To define the denotation of this operator, let A β i , A γ i be the FSRAs denoted by β i , γ i , respectively.

this is an instance of the common tradeoff of time versus space: FSRAs improve the network size at the cost of slower analysis time in some cases.


It accepts a set of regular expressions, representing a set of roots, and a list of patterns, each of which containing exactly n slots.

we introduce a dedicated regular expression operator for circumfixation and show how expressions using this operator are compiled into the appropriate FSRA.


Evidently, given an FSRA A, it can be verified in polynomial time that A is linearized.

we now introduce new regular expression operators, accounting for our motivating linguistic phenomena, and show how expressions using these operators are compiled into the appropriate FSRA.


The pair q c 0 = (q 0 , # n ) is called the initial configuration, and configurations with the first component in F are called final configurations.

for an n-bit binary incrementor, 2 n disjunctions have to be considered, and therefore a minimized transducer has to assign a separate state to each combination of bits, resulting in 2 n states and a similar number of transitions.


First a corpus is prepared and patterns from clusters C1-C3 are identified.

the SemCor patterns thus extracted did not need manual validation, since the noun concept pairs were always in a partwhole relation.


the ISS system can disambiguate any pair of concepts, provided they are in WordNet or can be classified by NERD.


is the frequency with which they occurred.

for the genitive cluster we used a training corpus of 27,961 positive examples (325 pairs of concepts in a part-whole relation extracted from corpus "A" and 27,636 extracted from WordNet as selected pairs) and 18,611 negative examples (the non-part-whole relations extracted from corpus "A").


Given this knowledge, the student is walked through the process of generating and playing a cosine wave.

the first provides a very detailed set of instructions for compiling and executing the coswave program and then playing the generated output signal in Cool Edit 2000.


This chapter contains no exercises for the student.

cepstral analysis is then used to build a rudimentary pitch tracker, which is applied to a speech example.


The final chapter (Bosch) warns again that languages without adequate HLT "run the risk of being marginalized in the global Information Society" (page 169).

the chapter is a good summary of Mt terms and concepts, and phases in its development; most interesting for language planners and policy-makers will be to learn that decades of Mt research have been largely unsuccessful, with its first paradigm -rule-based translation -largely abandoned as fruitless, and newer statistical methods yet unproven (several later chapters corroborate this conclusion).


Some of the classics in this group include Gale and Church on smoothing corpus counts, Hindle and Rooth on prepositional phrase attachment, and Briscoe and Carroll on parser evaluation.

and, inevitably, a reorganization of the School of Informatics's Web space means that the URL given in the book is no longer valid.


It's hard to fault the editors for this, given the principled stance many theoretical linguistics have taken against corpus-based methodology.

the largest is made up of papers on corpus design and methodology, such as Nelson Francis on the construction of the Brown corpus, Burnage and Dunlop on the British National Corpus, and Böhmová and Hajičová on the Prague Dependency treebank.


(page 68) Weyl of course contributed extensively to quantum mechanics and already then held the view that "classical logic does not fit in with quantum physics and is to be replaced by a kind of 'quantum logic' " (page 263).


But in the Consumption class, essen and trinken have strong preferences for a food object, whereas konsumieren allows a wider range of object types.

lee (2001) has shown that the skew divergence is an effective measure for distributional similarity in NlP.


k-means is able to cope with the high degree of perturbation: The resulting clusters improve significantly and are comparable with those based on preprocessed hierarchical clustering; this competitiveness vanishes with an increasing number of features.

in a first step each induced verb cluster is assigned a gold standard class according to which class captures the majority of the cluster members.


But there is scope here for logical inference, even more so than before; likewise, there are pitfalls, more than before.

this takes at most quadratic time (i.e., O(n 2 ), where n is the number of elements in the domain).


Since L succeeds in distinguishing the two target elements, it follows that they are the only mice greater than 10 cm.

for concreteness, we focus here on Dale and Reiter's Incremental Algorithm (IA).


The validity of these results can be debated (van Deemter 2004) but, taking them at face value, one could base different generation strategies on them.

there is no need to distinguish between the two at the level of CD.


The number of anti-collocations was successfully reduced to zero, except for sentence sets 4 and 6 where 1% of the anti-collocations remained.

the generic clusters produced by this module contain the concepts that nearsynonyms may involve (the peripheral concepts) as simple strings.


Unlike most other NLG systems, HALogen already incorporates some collocational knowledge implicitly encoded in its language model (bigrams or trigrams), but this is mainly knowledge of collocations between content words and function words.

for Xenon, however, we implemented a set of 22 simple rules that extract the actual peripheral concepts from the initial peripheral strings.


The near-synonyms of a core denotation in each language do not need to be in separate clusters; they can be part of one larger cross-linguistic cluster.

experiment 1 involved simple monolingual generation.


First, we discovered that Future Use could not be well modeled in the first WOZ experiment and that User Expertise and Interaction Pace could not be well modeled in the second WOZ experiment, i.e., the corresponding MLR models explained less than 10% of the variance.

we applied PARADISE to the data from both wOZ experiments (Hajdinjak and Mihelič 2004a).


Here, at each step, a single predictor is eliminated from the current regression model if its removal would increase the sum-of-squares differences between the observed and the predicted values, by not more than F out (some properly chosen constant, typically between 2 and 4) times the residual mean square RSS n−p , where n is the number of observations and p is the number of predictors.

if they do, small errors or variations in the values of sample observations can have a large effect on the weights in the performance function (1).


A long list of objective dialogue metrics (Danieli and Gerbino 1995;Smith and Gordon 1997) for dialogue evaluation, which can be calculated without recourse to human judgment, and subjective dialogue metrics (Shriberg, Wade, and Price 1992;Danieli and Gerbino 1995), which are based on human judgments, have been proposed.

while the task of the wizard in the first WOZ experiment was to perform speech understanding and dialogue management, the task in the second WOZ experiment was to perform only speech understanding, and the dialogue-management task was assigned to the newly implemented dialogue manager.


(Automated model checking is an easier task that was discussed in chapter 1.)

these storage mechanisms are found wanting.


But this implies that each natural language item accepts only arguments of some one fixed type.

and finally, Montague's version of IL is higher order, allowing quantification over entities and functions of any level.


If the claims made by the authors for their new intensional language are borne out by further research, then this is a very important addition to the literature on the foundations of semantics for natural language.-Francis Jeffry Pelletier, Simon Fraser University

this is not true for natural language, where conjunctions, verbs, and pretty much any functional term that accepts arguments at all can accept arguments of different types.


In a second experiment, we showed how error dictionaries may be used to improve the automated collection of translation correspondences, avoiding translation pairs with orthographic errors.

we obtained a list with 19, 265, 271 strings.


To extend this analogy, we define the answer set of a filter F w.r.t.

spelling errors and grammatical bugs result.


With respect to their function, an argument fills a role in the relation described by its associated head, whereas an adjunct predicates a separate property of its associate head or phrase.

lines 1 and 2 show that NVstatus is a better baseline than chance.


They trained a neural network to distinguish 13 classes of semantic relations.

the accuracy of LRA is significantly higher than the accuracies of VSM-AV and VSM-WMtS, according to the Fisher Exact test (Agresti 1990), but the difference between the two VSM accuracies is not significant.


we have also experimented with Non-negative Matrix Factorization (NMF) (Lee and Seung 1999), Probabilistic Latent Semantic Analysis (PLSA) (Hofmann 1999), Kernel Principal Components Analysis (KPCA) (Scholkopf, Smola, and Muller 1997), and Iterative Scaling (IS) (Ando 2000).


Early versions of SME only mapped identical relations, but later versions of SME allowed similar, nonidentical relations to match (Falkenhainer 1990).

vSM-Av refers to the vSM using Altavista's database as a corpus.


P = "in" P = "* of" P = "of *" P = "* *" freq("quart P volume") 4 1 5 19 freq("volume P quart") 10 0 2 16 more weight to columns (patterns) with frequencies that vary substantially from one row (word pair) to the next, and less weight to columns that are uniform.

to reduce the amount of computation required, we first find the 30 nearest neighbors for each pair, ignoring the alternate pairs (600 × 599 = 359, 400 cosines), and then apply the full LRA, including the alternates, to just those 30 neighbors (600 × 30 × 16 = 288, 000 cosines), which requires calculating only 359, 400 + 288, 000 = 647, 400 cosines.


On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM.

this is not a new problem for the VSM; it is the standard situation when the VSM is used for information retrieval.


The first question addressed in our experiments is whether or not corrections can be predicted significantly better than our baselines.

as another example, the turn yes help contains a REP correction, followed by a request for help.


Table 7 shows that, using only non-prosodic features (ASR, SYS, POS), corrections can still be predicted with an accuracy statistically equivalent to our best results.

using the best feature set from Table 7 (Raw+ASR+SYS+POS, DIA = PreTurn), Table 8 shows our results using 25-fold cross-validation.


This chapter truly lives up to the promise of the book title and it also serves many purposes: First, it is an excellent, self-contained introduction to large-margin methods for machine learning.

genuinely "new developments" in the area of statistical parsing are presented in the invited paper by Michael Collins (chapter 2).


The current volume complements other textbooks available now by offering an overview of MTT, providing a survey of the current state of Spanish NLP research, introducing the field of CL from the linguistic point of view (explaining what specific linguistic knowledge is involved in each "CL product" rather than introducing the techniques used in CL/NLP to create that product), providing plenty of examples and exercises, and making this book freely available on-line.

those using this textbook in the classroom should consider providing supplementary materials that discuss the fundamental algorithms and techniques for speech and language processing as well as certain linguistic areas, such as phonetics and pragmatics.


Part II is entitled "Temporal reasoning," and for the most part contains material from the 1980s (such as the classic papers on temporal logic by James Allen and Drew McDermott).

furthermore, on two occasions the editors intervene (in collaboration with others) to provide new articles that fill serious gaps.


As the authors note in the preface to the book, the book does not present formal language and automata theory, and the reader is referred to other, more suitable books.

although Wired for Speech is very accessible, especially to the non-scientist, it is written with an unusual bibliography style for an academic book: all references and details are given in a notes section at the end of the book, making up one third of the content.


Let us only recall here such approaches as: the lexicosemantic analysis by Katz and Postal (1964), who work with the notions of head and modifier when specifying selection restrictions; (ii) the distinction between surface-oriented constituent structure and the (underlying) functional structure in lexical functional grammar by Bresnan (1978) and Kaplan and Bresnan (1982); (iii) the above-mentioned case grammar by Fillmore, motivated by the conviction that Chomskyan deep structure (with its specification of "deep" subject as a constituent of S regardless of the (different) semantic relations of the given NP to the verb) is not deep enough to capture the real underlying structure of the sentence; and (iv) the consecutive introduction of theta roles into the government and binding theory.

it is not difficult to see an analogy between this view and a more modern and explicit treatment of the so-called centering theory (Grosz, Joshi, and Weinstein 1995) and its shifts of centers.


This means that for a given data set, both measures will lead to rejection of the null hypothesis at the same level of significance.

the data of six subjects were excluded after inspection of their responses revealed anomalies in their ratings.